通过注意模型优化验证码识别

Raghavendra A Hallyal, S. C, P. Desai, M. M
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引用次数: 0

摘要

从CAPTCHA中检索信息是一个关键部分,CAPTCHA中总是包含一些不需要的信息和需要的信息,因此注意技术可以方便地选择有用的信息,丢弃不需要的部分。在自然语言处理(NLP)和计算机视觉(CV)相结合的深度学习领域中,注意力概念已经成为一个非常重要的组成部分。注意机制在基于OCR的应用中得到了严格的应用,该应用要求生成选择的信息而不是所有可用的信息。我们的工作包括使用迁移学习模型和参数搜索模型两种不同的模型实现一般、全局和局部注意机制。由于注意OCR技术计算量大,需要对整个过程进行优化,因此我们建议使用参数搜索算法对CAPTCHA信息进行优化检索。该检索包括使用权值将训练时间从4.03分钟减少到3.33分钟,并且用于训练的训练图像数量比以前减少。我们用参数搜索模型对一般注意模型获得了最高的准确率(87.34%),用参数搜索模型对局部注意模型的计算量和训练时间比用参数搜索模型少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized Recognition Of CAPTCHA Through Attention Models
Information retrieval from the CAPTCHA is a crucial part, this CAPTCHA always contains some unwanted information along with required information, so attention technique comes in handy to select such useful information discarding the unwanted part. The attention concept has become a very important part in the field of deep learning which uses Natural Language Processing(NLP) and Computer Vision(CV). Attention mechanism is rigorously used in OCR based applications which requires generating of selected information rather than every information available. Our work includes implementation of general, global and local Attention mechanisms used with two different models which were transfer learning model and the parameter search model. As OCR with attention technique is computationally costly it is required to optimize the entire process so we suggest optimized retrieval of information from CAPTCHA using parameter search algorithm. This retrieval includes using weights that reduced the training time from 4.03 minutes to 3.33 minutes and the number of training images which were used for training were reduced than before. We obtained the highest accuracy of 87.34% for general attention with parameter search model and local attention model with parameter search model proved to have less computation and less training time than the general attention with parameter search model.
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